



import argparse
import os
import re

import packaging.version


UpperCAmelCase_		 =					"""examples/"""
UpperCAmelCase_		 =					{
    """examples""": (re.compile(r"""^check_min_version\(\"[^\"]+\"\)\s*$""", re.MULTILINE), """check_min_version(\"VERSION\")\n"""),
    """init""": (re.compile(r"""^__version__\s+=\s+\"([^\"]+)\"\s*$""", re.MULTILINE), """__version__ = \"VERSION\"\n"""),
    """setup""": (re.compile(r"""^(\s*)version\s*=\s*\"[^\"]+\",""", re.MULTILINE), r"""\1version=\"VERSION\","""),
    """doc""": (re.compile(r"""^(\s*)release\s*=\s*\"[^\"]+\"$""", re.MULTILINE), """release = \"VERSION\"\n"""),
}
UpperCAmelCase_		 =					{
    """init""": """src/transformers/__init__.py""",
    """setup""": """setup.py""",
}
UpperCAmelCase_		 =					"""README.md"""

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Tuple	,   _snake_case	:int	,   _snake_case	:List[str]				)  ->     Dict:
   with open(_snake_case	,   '''r'''	,   encoding='''utf-8'''	,   newline='''\n'''				) as f:
      _A      =       f.read()
   _A  ,    _A      =       REPLACE_PATTERNS[pattern]
   _A      =       replace.replace('''VERSION'''	,   _snake_case				)
   _A      =       re_pattern.sub(_snake_case	,   _snake_case				)
   with open(_snake_case	,   '''w'''	,   encoding='''utf-8'''	,   newline='''\n'''				) as f:
      f.write(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     List[Any]:
   for folder, directories, fnames in os.walk(_snake_case				):
      # Removing some of the folders with non-actively maintained examples from the walk
      if "research_projects" in directories:
         directories.remove('''research_projects'''				)
      if "legacy" in directories:
         directories.remove('''legacy'''				)
      for fname in fnames:
         if fname.endswith('''.py'''				):
            update_version_in_file(os.path.join(_snake_case	,   _snake_case				)	,   _snake_case	,   pattern='''examples'''				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Union[str, Any]	,   _snake_case	:Optional[int]=False				)  ->     Optional[Any]:
   for pattern, fname in REPLACE_FILES.items():
      update_version_in_file(_snake_case	,   _snake_case	,   _snake_case				)
   if not patch:
      update_version_in_examples(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Any:
   _A      =       '''🤗 Transformers currently provides the following architectures'''
   _A      =       '''1. Want to contribute a new model?'''
   with open(_snake_case	,   '''r'''	,   encoding='''utf-8'''	,   newline='''\n'''				) as f:
      _A      =       f.readlines()

   # Find the start of the list.
   _A      =       0
   while not lines[start_index].startswith(_start_prompt				):
      start_index += 1
   start_index += 1

   _A      =       start_index
   # Update the lines in the model list.
   while not lines[index].startswith(_end_prompt				):
      if lines[index].startswith('''1.'''				):
         _A      =       lines[index].replace(
             '''https://huggingface.co/docs/transformers/main/model_doc'''	,   '''https://huggingface.co/docs/transformers/model_doc'''	,   )
      index += 1

   with open(_snake_case	,   '''w'''	,   encoding='''utf-8'''	,   newline='''\n'''				) as f:
      f.writelines(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	)  ->     str:
   with open(REPLACE_FILES['''init''']	,   '''r'''				) as f:
      _A      =       f.read()
   _A      =       REPLACE_PATTERNS['''init'''][0].search(_snake_case				).groups()[0]
   return packaging.version.parse(_snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[Any]=False				)  ->     Dict:
   _A      =       get_version()
   if patch and default_version.is_devrelease:
      raise ValueError('''Can\'t create a patch version from the dev branch, checkout a released version!'''				)
   if default_version.is_devrelease:
      _A      =       default_version.base_version
   elif patch:
      _A      =       F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
   else:
      _A      =       F'''{default_version.major}.{default_version.minor + 1}.0'''

   # Now let's ask nicely if that's the right one.
   _A      =       input(F'''Which version are you releasing? [{default_version}]'''				)
   if len(_snake_case				) == 0:
      _A      =       default_version

   print(F'''Updating version to {version}.'''				)
   global_version_update(_snake_case	,   patch=_snake_case				)
   if not patch:
      print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''				)
      clean_main_ref_in_model_list()

def 	SCREAMING_SNAKE_CASE_							(	)  ->     str:
   _A      =       get_version()
   _A      =       F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
   _A      =       current_version.base_version

   # Check with the user we got that right.
   _A      =       input(F'''Which version are we developing now? [{dev_version}]'''				)
   if len(_snake_case				) == 0:
      _A      =       dev_version

   print(F'''Updating version to {version}.'''				)
   global_version_update(_snake_case				)
   print('''Cleaning main README, don\'t forget to run `make fix-copies`.'''				)
   clean_main_ref_in_model_list()


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   parser.add_argument("""--post_release""", action="""store_true""", help="""Whether this is pre or post release.""")
   parser.add_argument("""--patch""", action="""store_true""", help="""Whether or not this is a patch release.""")
   UpperCAmelCase_		 =					parser.parse_args()
   if not args.post_release:
      pre_release_work(patch=args.patch)
   elif args.patch:
      print("""Nothing to do after a patch :-)""")
   else:
      post_release_work()







"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def      UpperCAmelCase     (       _lowercase :   Union[str, Any]	)						-> Union[str, Any]:


	"""simple docstring"""




	lowerCAmelCase_      							=			os.path.join(args.tf_model_dir			,   '''parameters.json'''	)
	lowerCAmelCase_      							=			json.loads(open(_lowercase	).read()	)
	if not params:
		raise ValueError(
		    F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."""	)
	if not args.output.endswith('''.pt'''	):
		lowerCAmelCase_      							=			args.output + '''.pt'''
	lowerCAmelCase_      							=			OrderedDict()
	with tf.device('''/CPU:0'''	):
		lowerCAmelCase_      							=			tf.train.load_checkpoint(args.tf_model_dir	)
		lowerCAmelCase_      							=			reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase_      							=			reader.get_tensor(_lowercase	).astype(np.floataa	)
			if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
				continue
			if key_name.startswith('''pasts/'''	):
				if key_name.startswith('''pasts/mlp'''	):
					lowerCAmelCase_      							=			int(key_name[9]	)
				elif key_name.startswith('''pasts/out'''	):
					lowerCAmelCase_      							=			8
				lowerCAmelCase_      							=			'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/moe'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/switch_gating/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/softmlp/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
					lowerCAmelCase_      							=			key_name[-9:-7]
					for i in range(1_6	):
						lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
						lowerCAmelCase_      							=			(
						    vnp[i].transpose([1, 0]	).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/mlp'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/p1/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p1/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
					lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/p2/bias'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/ln'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.feed_forward.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/att'''	):
				lowerCAmelCase_      							=			int(key_name[9:].split('''/'''	)[0]	)
				if key_name.endswith('''/qkv/kernel'''	):
					lowerCAmelCase_      							=			vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase_      							=			state[:, 0, :, :]
					lowerCAmelCase_      							=			state[:, 1, :, :]
					lowerCAmelCase_      							=			state[:, 2, :, :]
					lowerCAmelCase_      							=			(
					    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			(
					    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
					    .transpose([1, 0]	)
					    .copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/o/kernel'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
					lowerCAmelCase_      							=			(
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/an'''	):
				lowerCAmelCase_      							=			int(key_name[8:].split('''/'''	)[0]	)
				if key_name.endswith('''/b'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.bias''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				elif key_name.endswith('''/g'''	):
					lowerCAmelCase_      							=			'''model.blocks.%d.self_attn.norm.weight''' % player
					lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif (
			    key_name.startswith('''model/wte'''	)
			    or key_name.startswith('''model/wpe'''	)
			    or key_name.startswith('''model/ete'''	)
			):
				lowerCAmelCase_      							=			{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
				    key_name[-3:]
				]
				lowerCAmelCase_      							=			'''model.%s.weight''' % nlayer
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
				if key_name.startswith('''model/wte'''	):
					lowerCAmelCase_      							=			'''lm_head.weight'''
					lowerCAmelCase_      							=			vnp.copy()  # same in embedded
					lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name.startswith('''model/wob'''	):
				lowerCAmelCase_      							=			'''final_logits_bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same in embedded
				lowerCAmelCase_      							=			state.reshape((1, -1)	)
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase_      							=			'''model.last_project.weight'''
				lowerCAmelCase_      							=			vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase_      							=			'''model.last_project.bias'''
				lowerCAmelCase_      							=			vnp.copy()  # same because it is one dimensional
				lowerCAmelCase_      							=			torch.tensor(_lowercase	)
	torch.save(_lowercase			,   args.output	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser(
		    description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
		)
		parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
		parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
		lowercase_							=							parser.parse_args()
		convert_tf_gptsan_to_pt(args)